Into:Aurelius
Simulating moral dilemmas to train AI.
As of · Jun 20, 09:17 UTC
Aurelius turns ethical dilemmas into training data. Miners write moral-conflict scenarios, validators run them through Google DeepMind's Concordia agent simulations, and the resulting transcripts are scored and used to teach language models how to reason about right and wrong.
What is Aurelius
Aurelius (subnet 37) is a Bittensor subnet built around AI alignment: the problem of getting AI systems to behave in line with human values. Rather than testing a finished model, it produces the raw material for studying moral reasoning. Contributors author structured ethical-dilemma scenarios, simulated characters play them out, and the transcripts are scored and turned into training data.
The simple version: It is like a writers' room for moral dilemmas. People script hard ethical situations (who gets the last dose of medicine, duty versus desire), simulated agents argue their way through them, and the resulting transcripts become study material for teaching AI to handle similar questions.
Centralized equivalent: The closest comparisons are the in-house alignment and red-team programs at labs like Anthropic, OpenAI, and Google DeepMind. There is no consumer-product equivalent. This is research infrastructure, not an app.
How it works:
- Miners serve a library of hand-authored scenario configs. Each one is a premise plus two characters with goals and assigned ethical philosophies (deontology, care ethics, and so on) and a forced choice. The miner returns configs to validators on request, round-robin.
- Validators put each scenario through an eight-stage check, run the accepted ones through a Concordia simulation, score the transcript, and set from the result.
Other research from the same neighborhood of the network.
